Systems and methods for extracting dates associated with a patient condition

Abstract

A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

What is claimed is: 
     
       1. A model-assisted system for extracting patient information, the system comprising:
 at least one processor programmed to:
 access a database storing a plurality of medical records associated with a plurality of patients; 
 input unstructured information included in the plurality of medical records into a first machine learning model, the first machine learning model being trained using first training data to identify patients associated with a condition; 
 identify, based on a first output from the first machine learning model, a subset of the plurality of patients, the subset of the plurality of patients being associated with the condition; 
 identify, based on an input by a user through a user interface, a date associated with a patient of the subset of the plurality of patients; 
 identify, within the plurality of medical records, one or more documents associated with the patient and having a timestamp prior to a cutoff date such that documents including one or more dates expressed in relative terms are excluded from the identified one or more documents, the cutoff date being based on a predetermined buffer period before or after the date; 
 input unstructured information included in the one or more documents into a second machine learning model, the second machine learning model being trained using second training data to indicate dates associated with the condition; 
 generate one or more pseudo-documents for input into the second machine learning model, the one or more pseudo-documents accounting for one or more dates within the unstructured information included in the one or more documents; 
 determine, based on a second output from the second machine learning model, whether the patient is associated with the condition relative to the date; and 
 generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date. 
 
 
     
     
       2. The system of  claim 1 , wherein the condition includes a diagnosed metastatic site associated with the patient. 
     
     
       3. The system of  claim 2 , wherein the diagnosed metastatic site includes a brain metastasis. 
     
     
       4. The system of  claim 1 , wherein the condition includes a diagnosed condition of the patient. 
     
     
       5. The system of  claim 1 , wherein determining whether the patient is associated with the condition includes determining whether the patient has been tested for the condition. 
     
     
       6. The system of  claim 1 , wherein identifying the date includes identifying at least one of a start date or an end date for a line of treatment for the patient in association with the condition. 
     
     
       7. The system of  claim 1 , wherein identifying the date includes identifying a plurality of dates and wherein determining whether the patient is associated with the condition includes determining whether the patient is associated with the condition relative to each of the plurality of dates. 
     
     
       8. The system of  claim 7 , wherein the plurality of dates each include a start dates for a particular line of treatment for the patient in association with the condition. 
     
     
       9. The system of  claim 1 , wherein the at least one processor is further programmed to:
 determine, using the first machine learning model and based on the unstructured information included in the plurality of medical records, whether each of the plurality of patients is associated with the condition; and 
 determine, using the second machine learning model and based on the unstructured information, whether each of the subset of the plurality of patients is associated with the condition relative to the date; and 
 wherein the output identifies a group of the subset of the plurality of patients associated with the condition relative to the date. 
 
     
     
       10. The system of  claim 1 , wherein the at least one processor is further programmed to transmit the output to at least one of a healthcare provider or a research entity. 
     
     
       11. A computer-assisted method for extracting patient information, the method comprising:
 accessing a database storing a plurality of medical records associated with a plurality of patients; 
 inputting unstructured information included in the plurality of medical records into a first machine learning model, the first machine learning model being trained using first training data to identify patients associated with a condition; 
 identifying, based on a first output from the first machine learning model, a subset of the plurality of patients, the subset of the plurality of patients being associated with the condition; 
 identifying, based on an input by a user through a user interface, a date associated with a patient of the subset of the plurality of patients; 
 identifying, within the plurality of medical records, one or more documents associated with the patient and having a timestamp prior to a cutoff date such that documents including one or more dates expressed in relative terms are excluded from the identified one or more documents, the cutoff date being based on a predetermined buffer period before or after the date; 
 inputting unstructured information included in the one or more documents into a second machine learning model, the second machine learning model being trained using second training data to indicate dates associated with the condition; 
 generating one or more pseudo-documents for input into the second machine learning model, the one or more pseudo-documents accounting for one or more dates within the unstructured information included in the one or more documents; 
 determining, based on a second output from the second machine learning model, whether the patient is associated with the condition relative to the date; and 
 generating an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date. 
 
     
     
       12. The method of  claim 11 , wherein the condition includes a diagnosed metastatic site associated with the patient. 
     
     
       13. The method of  claim 11 , wherein identifying the date includes identifying at least one of a start date or an end date for a line of treatment for the patient in association with the condition. 
     
     
       14. A non-transitory computer-readable medium storing instructions executable by at least one processor to perform a method, the method comprising:
 accessing a database storing a plurality of medical records associated with a plurality of patients; 
 inputting unstructured information included in the plurality of medical records into a first machine learning model, the first machine learning model being trained using first training data to identify patients associated with a condition; 
 identifying, based on a first output from the first machine learning model, a subset of the plurality of patients, the subset of the plurality of patients being associated with the condition; 
 identifying, based on an input by a user through a user interface, a date associated with a patient of the subset of the plurality of patients; 
 identifying, within the plurality of medical records, one or more documents associated with the patient and having a timestamp prior to a cutoff date such that documents including one or more dates expressed in relative terms are excluded from the identified one or more documents, the cutoff date being based on a predetermined buffer period before or after the date; 
 inputting unstructured information included in the one or more documents into a second machine learning model, the second machine learning model being trained using second training data to indicate dates associated with the condition; 
 generating one or more pseudo-documents for input into the second machine learning model, the one or more pseudo-documents accounting for one or more dates within the unstructured information included in the one or more documents; 
 determining, based on a second output from the second machine learning model, whether the patient is associated with the condition relative to the date; and 
 generating an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date. 
 
     
     
       15. The system of  claim 1 , wherein the one or more pseudo-documents are each associated with a document date matching the one or more dates within the unstructured information included in the one or more documents. 
     
     
       16. The system of  claim 1 , wherein the one or more pseudo-documents include information indicating a date as though text within the one or more pseudo-documents was written on the indicated date.